scholarly journals Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications

2018 ◽  
Vol 10 (11) ◽  
pp. 1684 ◽  
Author(s):  
Yu-Hsuan Tu ◽  
Stuart Phinn ◽  
Kasper Johansen ◽  
Andrew Robson
Keyword(s):  
Sensor Calibration ◽  
Unmanned Aerial Systems ◽  
Surface Reflectance ◽  
Radiometric Correction ◽  
Crop Monitoring ◽  
Horticultural Crops ◽  
Industry Standard ◽  
Multi Temporal ◽  
Aerial Systems ◽  
Bidirectional Reflectance Distribution

Multi-spectral imagery captured from unmanned aerial systems (UAS) is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS-based data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate bidirectional reflectance distribution function (BRDF) correction. Future UAS-based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

scholarly journals Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications

2018 ◽  
Author(s):  
Yu-Hsuan Tu ◽  
Stuart Phinn ◽  
Kasper Johansen ◽  
Andrew Robson
Keyword(s):  
Sensor Calibration ◽  
Surface Reflectance ◽  
Biophysical Properties ◽  
Horticultural Crop ◽  
Radiometric Correction ◽  
Crop Monitoring ◽  
Horticultural Crops ◽  
Production Parameters ◽  
Industry Standard ◽  
Multi Temporal

UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

scholarly journals Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

2020 ◽  
Vol 12 (22) ◽  
pp. 3831
Author(s):  
Marvin Ludwig ◽  
Christian M. Runge ◽  
Nicolas Friess ◽  
Tiziana L. Koch ◽  
Sebastian Richter ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Spatial Data ◽  
Forest Canopy ◽  
Cost Effective ◽  
Temporal Analysis ◽  
Unmanned Aerial Systems ◽  
Wide Range ◽  
Very High Spatial Resolution ◽  
Multi Temporal ◽  
Aerial Systems

Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.

scholarly journals Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

2020 ◽  
Author(s):  
Lucie Anne Eberhard ◽  
Pascal Sirguey ◽  
Aubrey Miller ◽  
Mauro Marty ◽  
Konrad Schindler ◽  
...  
Keyword(s):  
Snow Depth ◽  
Depth Map ◽  
Ground Truth ◽  
Unmanned Aerial Systems ◽  
Advantages And Disadvantages ◽  
Industry Standard ◽  
Accuracy And Precision ◽  
Mapping Techniques ◽  
Aerial Systems ◽  
Mean Square Errors

Abstract. Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have made significant progress in recent years and are suitable to accurately map snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pléiades), airplanes (Ultracam), Unmanned Aerial Systems UAS (eBee+) and ground-based (single lens reflex camera), were tested in a timely manner for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while UAS and ground-based photogrammetric imagery were acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements the root mean square errors (RMSEs) and the normalized median deviations (NMADs) are 0.52 m and 0.47 m for the Pléiades snow depth map, 0.17 m and 0.17 m for the Ultracam snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. Ground-based had to few measurements to be statistically relevant. When using the eBee+ snow depth map as ground truth, the RMSEs and NMADs are 0.44 m and 0.38 m for the Pléiades snow depth map, 0.12 m and 0.11 m for the Ultracam snow depth map, 0.21 and 0.19 m for the ground-based snow depth map. Because of the accuracy and precision of the Ultracam dataset we finally compared the Ultracam snow depth map to the Pléiades snow depth map over a large part of the Dischma valley and calculated a RMSE of 0.92 m and a NMAD of 0.65 m. By comparing for the first time more than two platforms, this study provides comparative measurements between platforms to evaluate the specific advantages and disadvantages of them for operational, spatially continuous snow depth mapping in alpine terrain over small and large areas.

scholarly journals A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests

2021 ◽  
Vol 13 (13) ◽  
pp. 2631
Author(s):  
Heather Grybas ◽  
Russell G. Congalton
Keyword(s):  
New Hampshire ◽  
Time Window ◽  
Early Spring ◽  
Optimal Time ◽  
Unmanned Aerial Systems ◽  
Species Classification ◽  
Blue Band ◽  
Multi Temporal ◽  
Aerial Systems ◽  
Improved Accuracy

Unmanned aerial systems (UASs) have recently become an affordable means to map forests at the species level, but research into the performance of different classification methodologies and sensors is necessary so users can make informed choices that maximize accuracy. This study investigated whether multi-temporal UAS data improved the classified accuracy of 14 species examined the optimal time-window for data collection, and compared the performance of a consumer-grade RGB sensor to that of a multispectral sensor. A time series of UAS data was collected from early spring to mid-summer and a sequence of mono-temporal and multi-temporal classifications were carried out. Kappa comparisons were conducted to ascertain whether the multi-temporal classifications significantly improved accuracy and whether there were significant differences between the RGB and multispectral classifications. The multi-temporal classification approach significantly improved accuracy; however, there was no significant benefit when more than three dates were used. Mid- to late spring imagery produced the highest accuracies, potentially due to high spectral heterogeneity between species and homogeneity within species during this time. The RGB sensor exhibited significantly higher accuracies, probably due to the blue band, which was found to be very important for classification accuracy and lacking in the multispectral sensor employed here.

scholarly journals MULTI-TEMPORAL IMAGE CO-REGISTRATION OF UAV BLOCKS: A COMPARISON OF DIFFERENT APPROACHES

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 9-16
Author(s):  
P. Garieri ◽  
M. Riboloni ◽  
G. Forlani ◽  
R. Roncella
Keyword(s):  
Environmental Changes ◽  
Satellite System ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Additional Information ◽  
Ground Control Points ◽  
Multi Temporal ◽  
Survey Accuracy ◽  
Global Navigation Satellite ◽  
Aerial Systems

Abstract. Traditionally, data co-registration of survey epochs in photogrammetry relied on Ground Control Points (GCP) to keep the reference system unchanged. In the last years, Unmanned Aerial Systems (UAV) are increasingly used in photogrammetric environmental monitoring. The diffusion of affordable UAV platforms equipped with GNSS (Global Navigation Satellite System) centimetre-grade receivers might reduce, but not eliminate, the need for GCP. Conversely, if GNSS-assisted orientation cannot be used or if additional ground control and reliability checks are required, alternatives to repeated GCP survey have been proposed, taking advantage of Structure from Motion (SfM) photogrammetry. In particular, co-registering different epochs image blocks together, identifying corresponding features, has been demonstrated as a viable and efficient approach. In this paper four different strategies easily implementable in a generic commercial photogrammetric software are presented and compared considering three different test sites in Italy subject to different amounts of environmental changes. The influence of the amount and distribution of inter-epoch corresponding points on the accuracy of the reconstruction is investigated. The results show that some of the tested strategies obtains very good results and can be used (although not needed) also in RTK centimetre-grade UAV surveys, leveraging the additional information coming from previous epochs survey to actually increase the survey accuracy and reliability.

scholarly journals Thermal Sensor Calibration for Unmanned Aerial Systems Using an External Heated Shutter

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 119
Author(s):  
Jacob Virtue ◽  
Darren Turner ◽  
Guy Williams ◽  
Stephanie Zeliadt ◽  
Matthew McCabe ◽  
...  
Keyword(s):  
Thermal Infrared ◽  
Field Trials ◽  
Sensor Calibration ◽  
Unmanned Aerial Systems ◽  
Thermal Sensor ◽  
Infrared Sensors ◽  
Calibration Source ◽  
Blackbody Source ◽  
Temperature Ranges ◽  
Aerial Systems

Uncooled thermal infrared sensors are increasingly being deployed on unmanned aerial systems (UAS) for agriculture, forestry, wildlife surveys, and surveillance. The acquisition of thermal data requires accurate and uniform testing of equipment to ensure precise temperature measurements. We modified an uncooled thermal infrared sensor, specifically designed for UAS remote sensing, with a proprietary external heated shutter as a calibration source. The performance of the modified thermal sensor and a standard thermal sensor (i.e., without a heated shutter) was compared under both field and temperature modulated laboratory conditions. During laboratory trials with a blackbody source at 35 °C over a 150 min testing period, the modified and unmodified thermal sensor produced temperature ranges of 34.3–35.6 °C and 33.5–36.4 °C, respectively. A laboratory experiment also included the simulation of flight conditions by introducing airflow over the thermal sensor at a rate of 4 m/s. With the blackbody source held at a constant temperature of 25 °C, the introduction of 2 min air flow resulted in a ’shock cooling’ event in both the modified and unmodified sensors, oscillating between 19–30 °C and -15–65 °C, respectively. Following the initial ‘shock cooling’ event, the modified and unmodified thermal sensor oscillated between 22–27 °C and 5–45 °C, respectively. During field trials conducted over a pine plantation, the modified thermal sensor also outperformed the unmodified sensor in a side-by-side comparison. We found that the use of a mounted heated shutter improved thermal measurements, producing more consistent accurate temperature data for thermal mapping projects.

LIGHTER-THAN-AIR (LTA) UNMANNED AERIAL SYSTEM (UAS) CARRIER CONCEPT FOR SURVAILLANCE AND DISASTER MANAGEMENT

2019 ◽  
Vol 3 ◽  
pp. 1255
Author(s):  
Ahmad Salahuddin Mohd Harithuddin ◽  
Mohd Fazri Sedan ◽  
Syaril Azrad Md Ali ◽  
Shattri Mansor ◽  
Hamid Reza Jifroudi ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Disaster Management ◽  
Unmanned Aerial Systems ◽  
Unmanned Aerial System ◽  
Border Control ◽  
Aerial Vehicles ◽  
Operational Safety ◽  
In Situ Methods ◽  
Aerial Systems

Unmanned aerial systems (UAS) has many advantages in the fields of SURVAILLANCE and disaster management compared to space-borne observation, manned missions and in situ methods. The reasons include cost effectiveness, operational safety, and mission efficiency. This has in turn underlined the importance of UAS technology and highlighted a growing need in a more robust and efficient unmanned aerial vehicles to serve specific needs in SURVAILLANCE and disaster management. This paper first gives an overview on the framework for SURVAILLANCE particularly in applications of border control and disaster management and lists several phases of SURVAILLANCE and service descriptions. Based on this overview and SURVAILLANCE phases descriptions, we show the areas and services in which UAS can have significant advantage over traditional methods.

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